Does Access to Finance Eradicate Poverty? A Case Study of Mudra Beneficiaries

Abstract

The main objective of this study was to investigate the impact of access to finance on the different dimensions of poverty. To achieve the objectives of the study, the participants/beneficiaries of the Mudra scheme were included and sample of target respondents was extracted through multistage random sampling technique. The sample for the study was taken from the Union Territory of Jammu and Kashmir of India. The study further utilized secondary data from the government official websites and lead banks. A paired t-test was applied to test the impact of access to finance across the various dimensions of poverty by constructing the Multidimensional Poverty Index(MPI), after checking the normality of the data. MPI incorporates dimensions such as education, health, and standard of living.The finding of the study revealed that dimensions of poverty responded positively to access to finance. The study shows that larger access to finance has helped in reducing the multidimensional poverty by having moderate, but positive impact on the standard of living, health, and education, thereby improving the lives of the poor. The present study identified that the level of impact of access to finance is moderate and further explains its importance for policy implications.

1. Introduction

India’s population has crossed the1.3 billion-mark and is expected to have 34.33% of youth by 2020 (Central Statistical Official, 2017). These figures give the country an advantage over the other nations in thecontext to manpower. But currently, the employment rate of India is confronting severe crises and falling continuously as a result of declining employment in the manufacturing, agricultural, and construction sectors (Mehrotra & Parida, 2019). According to the Ministry of Labour and Employment Report (2018/19), the labor work participation rate also registered declining trend in India. In addition to this, according to the report of think tank Centre for Monitoring Indian Economy (CMIE), the unemployment rate of India hits 7.52% in 2019 and this figure specifically hit the educated youth. It observed that the mounting ratio of labor work participation has not matched with the rate of employment. Meanwhile, India has a 21.9 % (269 million) of the population, which is living below the poverty line and some scholars explain the nexus between unemployment and poverty as having a positive relationship (Egunjobi, 2014; Omojolaibi & Omojolaibi, 2014). Akwara et al. (2013) also examined the relationship between unemployment, poverty, and insecurity and stated that unemployment causes poverty, and poverty further causes insecurity. There are several reasons associated with this, but the most prominent or basic reason is weak investment or financial constraints in the market. Hence, it is high time to address these issues by adopting anti-poverty policies or strategies that generate effective employment and simultaneously reduce poverty. Bangoura (2012) advocated that this is feasible with the collaborative development of strategies that fight poverty. The literature also suggests that financial development affects poverty positively by providing access by the poor to financial services (Clark, Xu, & Zou, 2003; Honohan, 2004).

The Reserve Bank of India (RBI) and the Government of India (GOI) have taken various initiatives to promote and provide adequate financial resources or services to everyone without any discrimination. These initiatives have taken the form of nationalization of major domestic banks, National Bank for Agriculture and Rural Development (NABARD), the creation of Regional Rural Banks, the establishment of Lead Bank Scheme, the formation of Microfinance institutions’ priority sector lending, linkage to self-help groups (SHGs) scheme with banks, Kisan Credit Card, etc, (Khan, 2005). The only purpose of these initiatives is to facilitate access by people to more adequate financial resources and services. Literature also suggests more financial inclusion and a strong relationship between financial development and economic growth (King & Levine, 1993; Beck et al., 2000; Beck & Levine, 2004; Levine, 2005).

Financial development is the most important component in the process of economic growth and contributes positively to the path of economic growth by the inclusion of efficient credit allocation and risk reduction (Jalilian & Kirkpatrick, 2002; Abu-Baber, 2008; Abu-Qarn, 2008). Microfinance institutions’ aim is to provide credit to the poor who have no access to commercial banks (Hermes & Lensink, 2007). Microfinance institutions provide credit without collateral, usually in relatively small amounts and for short periods (Gosh, 2013). Moreover, these institutions lend on an interest rate that is lower than the market rate (Hermes & Lensink, 2007). Also, microfinance is a banking service, which provides credit to low-income individual or group and create more opportunities for the most disadvantaged people. Moreover, the inclusion of financial resources is for the inclusion of people in the mainstream; this promotes productivity of people and leads to poverty reduction (Stiglitz, 1998/2001; Jalilian & Kirkpatrick, 2002; Odhiambo, 2010). These services lead to self-reliance by providing access to finance to poor individuals or groups (Sehrawat & Giri, 2016).

Some studies support this approachthat there is a positive impact of a microfinance program on income and expenditure (Silva, 2012; Morris & Barnes, 2005; Mishra;Hossain, 1988. Some other studies reveal that microfinance programs are significantly changing household expenditure (Boonperm et al., 2013; Berhane & Gardebroek, 2011; Mahjabeen, 2008; Khandker, 2005; Khandker, 2003). Meanwhile, still other studies show microfinance programs do not reach the poorest of the poor (Matin & Hulme, 2003; Mosley, 2001; Altay, 2007)and havean insignificant impact on reducing poverty (Morduch, 1998; Coleman, 2006). Kondo et al. (2008) examined the impact of a microfinance program in a rural area and reported that microfinance programs have an insignificant impact on income, expenditure, and the reduction of poverty. With these facts, this study assumes the pivotal importance of access to finance on poverty reduction. What kind of impact financial inclusion has on the dimensions of poverty? This paper attempts to answer this question by exploring linkages between financial inclusion and dimensions of poverty and provides further empirical evidence on the poverty-reducing effects of microfinance. Besides, the present study is different from others for the basic reason that this study has been conducted with the various dimensions of Multidimensional Poverty Index (comprising education, health, and standard of living) that are not used by other studies,generally restricted to single- dimensional variable like income or expenditure.

The study is structured into five sections: after a brief introduction in the first section, the second section presents the literature review; the third section describes the models, data, and relevant variables used in the study; the fourth section analyzes the empirical results, and the fifth section presents the conclusion and policy implications based on the empirical results of the study.

2. Literature Review

Overa few decades, the importance of an inclusive financial system is widely recognized and financial inclusion is projected as a policy priority in many countries (Kempson et al., 2004). This tool used by policymakers to stimulate economic agents to carry out economic activities leads to economic growth (Martinez, 2011). Financial inclusion is a broad concept that gives guarantees of ease-to-access, availability, and use of the formal financial system by all members of an economy (Sarma, 2008). It can favor underprivileged people by providing financial resources to become employed and increase their financial resources (Bruhn & Love, 2014; Chibba, 2009). Globally, microfinance delivers adequate resources that enhanced the quality of life, the standard of living, self-confidence, and livelihood strategies thereby increasing the socio-economic condition of the participants (Robinson (2001). Further, access to finance has a positive association with the education of children, nutritional status, and empowerment (Johnson & Rogaly, 1997). According to Wright (2000) and Littlefield et al.(2003), financial inclusion/access to finance tend to improve education, healthcare and hygiene, and nutritional indicators of the participants.

Keeping these aspects in consideration, the study develops the following hypotheses:

H₀. There is no significant impact of access to finance on the dimensions of poverty.

H_a. There is a significant impact of access to finance on the dimensions of poverty

3. Methods and Data Source

3.1. Data Source

The data used in the study were drawn from primary as well as secondary sources. Primary data was collected by using a well-structured interview schedule from December 2019 to February 2020. Detailed and in-depth interviews and direct personal investigating methods were utilized to extract the data from the beneficiaries of the Mudra Yojana scheme of Union Territory of Jammu and Kashmir (UTJK),respectively. Secondary data were obtained from different sources, including annual Statistical reports of Reserve Bank of India, and annual reports of Mudra Yojana published by Nodal Lead Bank of Jammu and Kashmir, Pradhan Mantri Mudra Yojana scheme.

3.2. Sample selection and sampling Design:

The multistage sampling technique was followed to extract the sample beneficiaries of the Mudra Yojana scheme. In the initial stage, the Jammu region of Union Territory of Jammu and Kashmir (UTJK) was selected randomly. In the second stage, four districts (Jammu, Kathua, Reasi, and Udhampur) out of ten districts were selected randomly. In the third stage, two tehsils(administrative divisions) from each district were selected for the study randomly (see Table 1). In the last stage, detailed lists of beneficiaries of Mudra Yojana were collected from the nodal Lead Bank of UTJK followed by information about beneficiaries, which was sorted tehsil-wise and respondents were selected randomly from it (Table 1).

Table 1: Multistage Sampling and Sampling Compositions

To reach out to the final sample size, pre-testing was done on 60 Mudra beneficiaries. After the tabulation of data pre-testing results was drawn, some items in the schedule were modified and few items were deleted;ultimately 19 items were retained for the final survey. The final sample size totaled 400 respondents on the basis of the following formula derived from Yamane (1973):

\(n=\frac N {1+N(e)^2}\) 

Where, n= Sample Size, N= Total Population, e= Tolerable error (0.05 or 95 percent)

The study was planned to be carried out with 400 beneficiaries of the Mudra Yojana scheme, but only 253 respondents responded to the survey in the assigned study area (Table 1). Many respondents didn’t take part in the survey due to various personal reasons. Thus, the final sample size for the study was 253 respondents, which suggest that the effective response rate of the respondents for the present study was 63.25 percent from the four districts of UTJK.

3.3. Impact Assessment

To measure the impact of access to finance across various dimensions of poverty (MPI), the comprehensive information was collected from the beneficiaries of Pradhan Mantri Mudra Yojana (PMMY) by using well-structured interview schedule and “pre and post” or “before and after” approach was used to observe the actual socio-economic condition of the respondents. In this study, only beneficiaries of Mudra yojana were included who have availed themselves of the loan and before FY 2017-2018. The information was collected in two rounds: in the firstround current socio-economic condition (after mediating process) of the respondents were collected; before conducting thesecond round the investigator gave a 20-minute break to respondents to help them recall the condition a year before (before mediating process) to avoid inconsistency.

3.4. Normality

To check normality, data was assessed graphically; Q-Q plot and Box plot illustrated and it indicated that the data were normally distributed. Second, it was tested with two statistical methods, namely,Skewness and Kurtosis, and the values are -.041 and -.482 of MPI pre-test and .154 and -.478 of MPI post-test. It lies between the limits of +1 to -1. This again shows that the data were normally distributed. Finally, Z-score was calculated and the values fall in between +1.96 to -1.96. This shows that the data were normally distributed.

3.5. Concept of Multidimensional Poverty and Measurement

Poverty is a global issue and a challenge for policymakers to determine the true picture of poverty. Moreover, there are two international standard measures: first income-based; this measure considered income or consumption expenditure to determine the poverty and it sees poverty as a result of the inability of the individual or family to congregate their basic needs (World Bank, 2000). According to the World Bank, a person earning less than $1.90 a day (PPP, 2011) is considered to be poor (World Bank, 2000). This measure is well-defined and straightforward to determine poverty and to beimplemented across the countries (Coromaldi & Zoli, 2012). But from the last two decades, the debate is going on between the researchers for a different or alternative approach, which is different from the income-based approach to measuring true poverty. Sen (1992) stated poverty is not due to lack of income, but it is deprivation in basic human capabilities. Deprivations in the basic capabilities of the individual or family includes illiteracy, malnutrition, persistent disease, actual living, and empowerment. These capabilities are important and fundamentally valuable. Enhancing these basic capabilities of poor people through education and health care will improve their productivity and income (Sen, 1999). Therefore, multidimensional poverty measurement based on basic capability can replicate the real circumstances of poverty more accurately, and the poverty should be treated as multidimensional (Alkire, 2002, Alkire & Foster, 2007 & 2011, Wang & Alkire, 2009) and this second approach considered multiple variables to determine the poverty and based on deprivation know as Multidimensional Poverty Index MPI. Various studies on multidimensional assessment of poverty (Alkire & Seth, 2013; Alkire & Roche, 2013; Alkire et al, 2013; Alkire & Santos, 2010; UNDP, 2010 &2013) considered only three dimensions, namely,health, education, and living standard, which is shown in table 2.

Brief about dimensions of MPI given below:

1. Education:

a. Year of schooling: Deprived if no household member has completed at least one year of schooling,otherwise not deprived.

b. School attendance: Deprived if no children are attending school up to the age at which they should finish class 6,otherwisenot deprived.

2. Health:

a. Nutrition level: Deprived if undernourishment of an adult under 70 years of age,otherwisenot deprived. 

b. Child mortality: Deprived if two or more children have died in the household,otherwisenot deprived.

3. Standard of living:

a. Cooking fuel: Deprived if the household cooks with dung or wood,otherwisenot deprived.

b. Improved Sanitation: Deprived if the is no sanitation/toilet facility,otherwise not deprived.

c. Improved drinking water: Deprived if the household does not have access to safe drinking water or safe water is more than a 45-minute walk (round trip),otherwisenot deprived.

d. Electricity: Deprived if the household has no electricity,otherwisenot deprived.

e. Floor: Deprived if the household has a dirt, sand, or dung floor,otherwise not deprived.

f. Assets: Deprived if the household has no assets (radio, mobile phone, refrigerator,etc.)and no car, otherwise not deprived.

In the present study, the investigator has followed the same dimensions as in Table 2 with slight modification in some indicators. To check the standard of living one more variable housing condition was added in the index. In the health dimension, the nutrition level wasmeasured with Body Mass Index (BMI=kg/m², weight/height) approach, and child mortality variable is substituted with expenditure on healthcare. In the education dimension, the year of schooling is substituted with expenditure on education. Further, Alkire and Foster’s(2007 & 2011) methodology was adapted to assign the weights (see Table 2) to the variables and cutoff point to estimate the deprivation level.

The following steps were taken to estimate or check the Multidimensional PovertyIndex. In the first step, to identify the deprivation level investigator applies deprivation cutoff to each indicator. Indicators deprivation cut off denoted as z_i,= x_{i ,} where i = person. Each indicator was coded with binary numbers as “1 = deprived or poor” and “0 = not deprived”. If indicator x_i is less than z_i then the person is considered to be deprived or poor else not deprived or nonpoor.

Table 2: Indicators of Multidimensional Poverty Index

After putting deprivation cutoff to each indicator, weights are assigned to each indicator. There are eleven indicators in the present study, indicators under education and health have been assigned 1/6 weight each, and indicators under living standard have assigned 1/21 weight.

After the deprivation cut off and weight, the deprivation score are assigned to each person according to their indicators. 

\(c_i =w_1 I_1 +w_2I_2 + \dots + w_d I_d\) 

Where, c_i= deprivation score, I_i=1 if deprived; else I_i = 0 for not deprived and w_i = weight attached to indicator i with \(\sum ^d _{I=i} w_i =1\)

After getting deprivation score, those individuals or household having score equal or greater than the poverty cut-off i.e. c_i >= k (k=1/3) are considered to be poor else considered nonpoor.

3.6. Tools of Analysis

After the collection of data, the following steps were taken: first, data were sorted by category; second, data were edited; and third,data were arranged in a sequential manner. During the process of editing certain errors were detected and erased. Excel and SPSS 21.0 were used to tabulate the data. Statistical tools like percentage, average, scaling techniques, and paired t-test were used to analyze the data. A paired t-test was used to analyze the impact of access to finance on dimensions of poverty/indicators of multidimensional poverty among beneficiaries of Mudra yojana in UTJK.

4. Results and Discussions

As per the socio-economic profile of respondents,the majority is male and belongs in the 36-to-45 age group. Only 8.6% of respondents are illiterate and the majority of them have schooling up to 10+2. A maximum number of respondents have availed themselves of loans to establish or expand their businesses. 

PMMY (Pradhan Mantri Mudra Yojana) was launched on April 8, 2015, in India by current serving Prime Minister of India Shri Narendra Damodardas Modi to provide financial support and services to small entrepreneurs in their comprehensive economic and social development. Mudra’s mission is “to create an inclusive, sustainable, and value-based entrepreneurial culture, in collaboration with our partner institutions in achieving economic success and financial security” (Mudra.org).This scheme (Mudra Bank) lend through intermediaries bank (see Figure 1) and borrowers can obtain loan from the concerned banks. This scheme has also flagged off in UTJK in the same year.Further, in the opening year, about 57,974 (2015/16) beneficiaries have taken financial support from the scheme in UTJK, and in the subsequent years the number of beneficiaries has increased from 89,712 in 2016/17 to 103,125 in 2017/18 and 691,027 in 2018/19. Similarly, the amount provided to beneficiaries for the given three financial years is as follows: 1185.13 crores in FY 2015/16, 1845.37 crores in FY in 2016/17, 2460.00 crores in FY 2017/18, and 1492.76 in FY 2018/19 (Government of India, 2019 and UTLBC, J&K, 2019). These figures reflect the Government of India (GOI) injected around 2 crores per beneficiaries through this scheme from its beginning years 2015/16 until 2018/19. The percentage change in lending amount from 2015/16 to 2016/17 is 16% and from 2016/17 to 2017/18 is 33% (excluding FY 2018/19).

But the question that arises is how much this facility has benefitted the recipients in the given years. There could be two possibilities: either they benefited or they didnot benefited. The impact of financial inclusion on socio- economic variables or dimensions of MPI of the respondents is reflected in Table 3.

Figure 1: Flow of financial resources from Mudra bank to borrowers

4.1. Impact of Access to Finance on Dimensions of Poverty

Literature suggests a two-dimensional impact of access to finance or financial inclusion on socio-economic factors. Onthe one hand, it shows positive impact (Khandker, 2005; Pitt & Khandker, 1998; Khandker, 2003) and on the other hand, it shows an insignificant impact (Matin & Hulme, 2003; Mosley, 2001; Altay, 2007; Morduch, 1998; Coleman, 1999 & 2006).

The present study’s findingsdocuments a significant impact of microfinance on the dimensions of poverty. The empirical result of the study reported that multidimensional poverty (MPI) declined from 503.16 percent to 431.23 percent (Table 3). Thus, it indicates the fact that ‘access to finance’ can eradicate poverty.

What is the impact of ‘access to finance’ on different dimensions of multidimensional poverty? The study has revealed that the largest reduction in deprivation,which fell 18 percent,has been seen in the standard of living, followed by heath (10% reduction) and education (9% reduction) (Table 3). Inside the dimension of the standard of living it has been observed that the standard of living of people increased because of increase in financial access, which enabled increase in their income resulting in increase in their purchasing power and expenditure level. This has further resulted in the increase in the durable assets, and further positive changes like clean floor, improved clean drinking water facilities, clean cooking fuel (LPG, BIOGAS) and cemented roof of the house except for electricity because they already were having this facility. Indicators of health status show that increase in access to finance has resulted in increase in consumption expenditure on healthcare along with slight improvement in the nutrition level. Similarly, indicators of education also show that student’s attendance has improved in the schools and universities after increase in access to finance. Increase in access to finance has also enhanced expenditure on education. Thus, the present study concludes that the increase in financial inclusion or increase in access to finance has significantly impacted the different indicators or multi-dimensions of poverty. Thus, it could be stated that this increase in access to finance has helped in improving the socio-economic status of the target population. 

The study points to the positive impact of theincrease in financial inclusion on poverty reduction, but also raises concerns about marginal percentage change in the mean value of MPI1 5.0316 pre-test to MPI2 4.3123 post-test, which is only 14%. This low percentage indicates the moderate nature of the impact of access to finance on poverty. The moderate impact of increase in financial access or increase in financial inclusion in this case could be due to intervention of various factors like prevalence of risk aversion behavior within the low income group resulting in lower probability of greater increase in income. Hulme and Mosley (1996) stated that borrowers from better-endowed layers benefited (risk- lover) more than borrowers living below the poverty line because they are very conservative (risk-averse) in nature. The reason is borrowers with more income are willing to take more risks by investing more in new capital than the poor groups. Other suggest that misaddressed government- backed programs (Binswanger & Khandker, 1995; Khaki & Sangmi, 2017) and intervene/influence of political power lead to the diversion of funds (Todd, 1996; Pulley, 1989; Coleman, 1999) and people used these funds to purchase land rather than complete their proposed work (Todd, 1996) and programs/schemes designed for livelihood promotion, such as microfinance, can benefit poor households, but do not reach directly the hardcore poor (Rahman & Hossain, 1995; Hashemi, 1997, 2001; Montgomery, Bhattacharya& Hulme, 1996).

Table 3: Result of t-test 

Note: ^aThe correlation and t cannot be computed because the standard error of the difference is 0. 

(1. SOL (Standard of living) = house status + water facilities + Electricity + Floor + Cooking + Durable Assets + Sanitization. 

2. Education= Expenditure on education + Child attendance. 3. Health= Nutrition + Expenditure on healthcare.)

5. Conclusions

This study clearly indicates the positive impact of increase in access to finance on various dimensions of poverty and henceforth its eradication. The standard of living, health condition, or expenditure on healthcare and expenditure on education or student attendance has been improved through the mediating process (access to finance). It is, thus, recommended that, along with various policy aspects, financial inclusion and increase in access to finance should be given priority in policy framework meant for poverty eradication. This study also indicates certain issues like the outreach and effectiveness of the scheme because of the marginal impact of financial access on dimensions of poverty. These factors inhibiting the positive impact of increase in access to finance are critical and should be studied separately. Identification of such factors demand separate study and is crucial to enhance the effectiveness of impact of financial inclusion on poverty. Even these other factors have variability as per the regional variations in the country or a particular region. Thus, specific regionally-base studies are necessary for evaluating such factors that affects the level of impact of financial inclusion on poverty.